Association of Epigenetic Age Acceleration and Mitochondrial <scp>DNA</scp> ‐Based Aging Metrics Provides Insights Into Mechanisms of Aging‐Related Diseases
Bibliographic record
Abstract
Investigating the interplay between mitochondrial DNA (mtDNA) variations and epigenetic aging metrics may elucidate biological mechanisms associated with age-related diseases. We estimated epigenetic age acceleration (EAA) metrics from DNA methylation data and derived mtDNA metrics, including heteroplasmic variants and mtDNA copy number (mtDNA CN) from whole genome sequencing. Linear regressions and meta-analyses were conducted to assess associations between EAA and mtDNA metrics, adjusting for chronological age, self-identified sex, and other covariates in 6,316 participants (58% female, 41% non-White Americans). Mediation analysis was conducted to examine whether EAA mediated the relationship between mtDNA CN and metabolic traits. A higher burden of rare heteroplasmic variants was associated with accelerations of first-generation EAA metrics, while a lower level mtDNA CN was associated with accelerations of second- and third-generation EAA metrics. For example, one standard deviation (SD) higher MSS, a score based on the predicted functions of rare heteroplasmic variants, was associated with a 0.22-year higher EAA by the Hannum method (p = 1.3E-6) among all participants, while one SD lower mtDNA CN was associated with higher DunedinPACE (β = -0.005, p = 6.0E-4). No significant association was observed between the heteroplasmy burden of common variants and EAAs. Furthermore, we observed DunedinPACE mediated 11.1% and 10.8% of the associations of mtDNA CN with obesity and T2DM in older FHS participants, respectively. Our analysis indicated that higher levels of heteroplasmy burden of rare variants and lower mtDNA CN were associated with accelerated epigenetic aging, and these associations showed stronger magnitudes among older participants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".